We present an end-to-end system for detecting and clustering faces by identity in full-length movies. Unlike works that start with a predefined set of detected faces, we consider the end-to-end problem of detection and clustering together. We make three separate contributions. First, we combine a state-of-the-art face detector with a generic tracker to extract high quality face tracklets. We then introduce a novel clustering method, motivated by the classic graph theory results of Erd\H{o}s and R\'enyi. It is based on the observations that large clusters can be fully connected by joining just a small fraction of their point pairs, while just a single connection between two different people can lead to poor clustering results. This suggests clustering using a verification system with very few false positives but perhaps moderate recall. We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme. Finally, we define a novel end-to-end detection and clustering evaluation metric allowing us to assess the accuracy of the entire end-to-end system. We present state-of-the-art results on multiple video data sets and also on standard face databases.

该研究提出了一种端到端的系统，用于在电影全长中检测和聚类面部，并基于先前的图论结果，介绍了一种新的聚类方法，并使用一种验证方法来描述正确率，并定义了一种新的端到端的检测和聚类评估度量，以评估整个系统的准确性。在多个视频数据集和标准人脸数据库上呈现出最先进的结果。

基于 Erdős-Rényi 聚类的电影人脸检测与演员聚类